I've been getting a lot of questions lately about how to start with machine learning. And I get it, the field moves fast, there are thousands of tutorials online, and it can feel like you need a PhD just to understand where to start.
You don't (i promise). This week, I'm sharing the exact roadmap I wish someone had given me when I first got curious about ML.
Step 1: Build Your Math Intuition 🧮
Before touching any code, you need a basic grasp of three areas: linear algebra, calculus, and probability. Don't panic, we're not talking about proving theorems here. You just need enough intuition to understand what's happening under the hood when you train a model.
There's a fantastic free resource called Mathematics for Machine Learning that covers exactly what you need. Focus on the core concepts, skip the deep proofs, and you'll have a solid foundation that makes everything else way less confusing.
Step 2: Get Comfortable with the Tools ⚒️
ML has its own ecosystem of tools, and getting familiar with them early saves a lot of headaches later.
Start with Jupyter Notebooks, it's where most ML work happens. Then pick up the data science essentials: Pandas for handling data, NumPy for number crunching, and Matplotlib for visualizations.
Once you're comfortable there, move on to the ML-specific libraries. Scikit-Learn is perfect for classical algorithms, while TensorFlow and PyTorch handle the deep learning side. If you prefer a guided approach, DataCamp and FreeCodeCamp offers solid interactive courses on all of these.
Step 3: Learn from the Best 👨🏫
With so many courses out there, it's easy to fall into tutorial hell. Here are three that are actually worth your time:
freeCodeCamp's ML course — great free starting point with hands-on coding
Andrew Ng's Machine Learning Specialization — the gold standard for understanding core concepts
Stanford's CS229 lectures — free on YouTube, goes deeper into the math if you want it
Step 4: Build Something Real 🏗️
Theory only gets you so far. At some point, you need to get your hands dirty.
Kaggle is the perfect playground. Start with their beginner competitions, things like predicting Titanic survivors or house prices. They're designed for newcomers and have tons of community notebooks to learn from.
After that, build something that matters to you. A music recommendation engine, an image classifier for your photos, a model that predicts something you actually care about. Personal projects stick with you in a way tutorials never will and it feels way more natural to talk about them in interviews. I personally build an image classifier for CT because I found that topic very fascinating and cool.
The Bottom Line
There's no secret shortcut here. Math foundations, the right tools, quality courses, and real projects. That's it. The people who succeed aren't necessarily the smartest, they're the ones who pick a path and keep showing up.
💡 My Recommendation of the Week:
Shame on me but this time I have to put the full PDF as my recommendation because I can’t put so many Links in one E-Mail (I’m afraid to end up in the spam folder haha) https://docs.google.com/document/d/1wMeVQu16cRK2-w2aDMx_8uNZLzGv6KAVnDd89oTB3JA/edit?usp=sharing
If you have any wishes regarding topics I should cover in future newsletter please reach out! :)
Have a nice week,
Chris